code: def test_randomizedsearchcv_refit(u1_ml100k):
      """Test refit method of RandomizedSearchCV class."""
  
      data_file = os.path.join(
          os.path.dirname(__file__), "./u1_ml100k_test"
      )
      data = Dataset.load_from_file(data_file, Reader("ml-100k"))
  
      param_distributions = {
          "n_epochs": [5],
          "lr_all": uniform(0.002, 0.003),
          "reg_all": uniform(0.4, 0.2),
          "n_factors": [2],
      }
  
      # assert rs.fit() and rs.test will use best estimator for mae (first
      # appearing in measures)
      rs = RandomizedSearchCV(
          SVD,
          param_distributions,
          measures=["mae", "rmse"],
          cv=2,
          refit=True,
      )
      rs.fit(data)
      rs_preds = rs.test(data.construct_testset(data.raw_ratings))
      mae_preds = rs.best_estimator["mae"].test(
          data.construct_testset(data.raw_ratings)
      )
      assert rs_preds == mae_preds
  
      # assert rs.fit() and rs.test will use best estimator for rmse
      rs = RandomizedSearchCV(
          SVD,
          param_distributions,
          measures=["mae", "rmse"],
          cv=2,
          refit="rmse",
      )
      rs.fit(data)
      rs_preds = rs.test(data.construct_testset(data.raw_ratings))
      rmse_preds = rs.best_estimator["rmse"].test(
          data.construct_testset(data.raw_ratings)
      )
      assert rs_preds == rmse_preds
      # test that predict() can be called
      rs.predict(2, 4)
  
      # assert test() and predict() cannot be used when refit is false
      rs = RandomizedSearchCV(
          SVD,
          param_distributions,
          measures=["mae", "rmse"],
          cv=2,
          refit=False,
      )
      rs.fit(data)
      with pytest.raises(ValueError):
          rs.test(data.construct_testset(data.raw_ratings))
      with pytest.raises(ValueError):
          rs.predict("1", "2")
  
      # test that error is raised if used with load_from_folds
      rs = RandomizedSearchCV(
          SVD,
          param_distributions,
          measures=["mae", "rmse"],
          cv=2,
          refit=True,
      )
      with pytest.raises(ValueError):
          rs.fit(u1_ml100k)
  
file_path: Surprise/tests/test_search.py
imports: import os
  import numpy
  import pytest
  from scipy.stats import randint, uniform
  from surprise import Dataset, Reader, SVD
  from surprise.model_selection import cross_validate, GridSearchCV, KFold, PredefinedKFold, RandomizedSearchCV
label_desc: Use image node_id 12 for calling a global function with example usage: test_randomizedsearchcv_refit(u1_ml100k) without return types